Climate Dynamics

, Volume 53, Issue 1–2, pp 173–192 | Cite as

The impact of climate model sea surface temperature biases on tropical cyclone simulations

  • Wei-Ching HsuEmail author
  • Christina M. Patricola
  • Ping Chang


Sea surface temperature (SST) patterns both local to and remote from tropical cyclone (TC) development regions are important drivers of the variability of TC activity. Therefore, reliable simulations and predictions of TC activity depend on a realistic representation of tropical SST. Nevertheless, severe SST biases are common to the current generation of global climate models, especially in the tropical Pacific and Atlantic. These biases are strongly positive in the southeastern tropical basins, and negative, but weaker, in the northwestern tropical basins. To investigate the impact of the tropical SST biases on simulated TC activity, an atmospheric-only tropical channel model was used to conduct several sets of ensemble simulations. The simulations suggest an underrepresentation in Atlantic TC activity caused by the Atlantic cold bias alone, and an overrepresentation in Eastern North Pacific (ENP) TC activity due to the Atlantic cold bias and Pacific warm bias jointly. While the local impact of SST biases on TC activity is generally induced by the local anomalous SST and the associated changes in atmospheric conditions, the remote impact of the Atlantic bias on the ENP TCs is strongly driven by the change in topographically forced regional circulation. Moreover, an eastward shift in Western North Pacific TCs was generated by the Pacific SST biases, even though basin-wide TC activity indicators change insignificantly. The results indicate the importance of considering SST bias effects on simulated TC activity in climate model studies and highlight key regions where reducing SST biases could potentially improve TC representation in climate models.


SST bias Tropical cyclones Tropical channel model Climate model bias 



The authors wish to thank the editor and two anonymous reviewers for comments that greatly improved the quality of this paper. This research is supported by U.S. National Science Foundation Grants OCE-1334707, AGS-1347808 and AGS-1462127, and National Oceanic and Atmospheric Administration Grant NA13OAR4310136. PC acknowledges the Natural Science Foundation of China (41490644 and 41490640). C.M.P. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division, Regional & Global Climate Modeling Program, under Award Number DE-AC02-05CH11231. High-performance computing resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE). Simulations were performed at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin and the Texas A&M Supercomputing Facility.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of OceanographyTexas A&M UniversityCollege StationUSA
  2. 2.Climate and Ecosystem Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Department of Atmospheric ScienceTexas A&M UniversityCollege StationUSA
  4. 4.Physical Oceanography Laboratory/Qingdao Collaborative Innovation Center of Marine Science and TechnologyOcean University of ChinaQingdaoChina

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